Added model cards for Tagalog ELECTRA models (#7996)

Co-authored-by: Jan Christian Blaise Cruz <jcblaise@Blaises-MacBook-Pro.local>

model_cards/jcblaise/electra-tagalog-base-cased-discriminator/README.md

+---
+language: tl
+tags:
+- electra
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# ELECTRA Tagalog Base Cased Discriminator
+Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
+
+This is the discriminator model, which is the main Transformer used for finetuning to downstream tasks. For generation, mask-filling, and retraining, refer to the Generator models.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/electra-tagalog-base-cased-discriminator', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-base-cased-discriminator', do_lower_case=False)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/electra-tagalog-base-cased-discriminator')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-base-cased-discriminator', do_lower_case=False)
+```
+Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
+
+## Citations
+All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
+
+```
+@article{cruz2020investigating,
+  title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
+  author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
+  journal={arXiv preprint arXiv:2010.11574},
+  year={2020}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## Contact
+If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph

model_cards/jcblaise/electra-tagalog-base-cased-generator/README.md

+---
+language: tl
+tags:
+- electra
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# ELECTRA Tagalog Base Cased Generator
+Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
+
+This is the generator model used to sample synthetic text and pretrain the discriminator. Only use this model for retraining and mask-filling. For the actual model for downstream tasks, please refer to the discriminator models.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/electra-tagalog-base-cased-generator', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-base-cased-generator', do_lower_case=False)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/electra-tagalog-base-cased-generator')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-base-cased-generator', do_lower_case=False)
+```
+Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
+
+## Citations
+All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
+
+```
+@article{cruz2020investigating,
+  title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
+  author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
+  journal={arXiv preprint arXiv:2010.11574},
+  year={2020}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## Contact
+If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph

model_cards/jcblaise/electra-tagalog-base-uncased-discriminator/README.md

+---
+language: tl
+tags:
+- electra
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# ELECTRA Tagalog Base Uncased Discriminator
+Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
+
+This is the discriminator model, which is the main Transformer used for finetuning to downstream tasks. For generation, mask-filling, and retraining, refer to the Generator models.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/electra-tagalog-base-uncased-discriminator', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-base-uncased-discriminator', do_lower_case=False)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/electra-tagalog-base-uncased-discriminator')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-base-uncased-discriminator', do_lower_case=False)
+```
+Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
+
+## Citations
+All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
+
+```
+@article{cruz2020investigating,
+  title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
+  author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
+  journal={arXiv preprint arXiv:2010.11574},
+  year={2020}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## Contact
+If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph

model_cards/jcblaise/electra-tagalog-base-uncased-generator/README.md

+---
+language: tl
+tags:
+- electra
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# ELECTRA Tagalog Base Uncased Generator
+Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
+
+This is the generator model used to sample synthetic text and pretrain the discriminator. Only use this model for retraining and mask-filling. For the actual model for downstream tasks, please refer to the discriminator models.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/electra-tagalog-base-uncased-generator', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-base-uncased-generator', do_lower_case=False)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/electra-tagalog-base-uncased-generator')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-base-uncased-generator', do_lower_case=False)
+```
+Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
+
+## Citations
+All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
+
+```
+@article{cruz2020investigating,
+  title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
+  author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
+  journal={arXiv preprint arXiv:2010.11574},
+  year={2020}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## Contact
+If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph

model_cards/jcblaise/electra-tagalog-small-cased-discriminator/README.md

+---
+language: tl
+tags:
+- electra
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# ELECTRA Tagalog Small Cased Discriminator
+Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
+
+This is the discriminator model, which is the main Transformer used for finetuning to downstream tasks. For generation, mask-filling, and retraining, refer to the Generator models.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/electra-tagalog-small-cased-discriminator', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-small-cased-discriminator', do_lower_case=False)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/electra-tagalog-small-cased-discriminator')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-small-cased-discriminator', do_lower_case=False)
+```
+Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
+
+## Citations
+All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
+
+```
+@article{cruz2020investigating,
+  title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
+  author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
+  journal={arXiv preprint arXiv:2010.11574},
+  year={2020}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## Contact
+If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph

model_cards/jcblaise/electra-tagalog-small-cased-generator/README.md

+---
+language: tl
+tags:
+- electra
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# ELECTRA Tagalog Small Cased Generator
+Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
+
+This is the generator model used to sample synthetic text and pretrain the discriminator. Only use this model for retraining and mask-filling. For the actual model for downstream tasks, please refer to the discriminator models.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/electra-tagalog-small-cased-generator', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-small-cased-generator', do_lower_case=False)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/electra-tagalog-small-cased-generator')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-small-cased-generator', do_lower_case=False)
+```
+Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
+
+## Citations
+All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
+
+```
+@article{cruz2020investigating,
+  title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
+  author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
+  journal={arXiv preprint arXiv:2010.11574},
+  year={2020}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## Contact
+If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph

model_cards/jcblaise/electra-tagalog-small-uncased-discriminator/README.md

+---
+language: tl
+tags:
+- electra
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# ELECTRA Tagalog Small Uncased Discriminator
+Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
+
+This is the discriminator model, which is the main Transformer used for finetuning to downstream tasks. For generation, mask-filling, and retraining, refer to the Generator models.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/electra-tagalog-small-uncased-discriminator', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-small-uncased-discriminator', do_lower_case=False)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/electra-tagalog-small-uncased-discriminator')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-small-uncased-discriminator', do_lower_case=False)
+```
+Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
+
+## Citations
+All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
+
+```
+@article{cruz2020investigating,
+  title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
+  author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
+  journal={arXiv preprint arXiv:2010.11574},
+  year={2020}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## Contact
+If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph

model_cards/jcblaise/electra-tagalog-small-uncased-generator/README.md

+---
+language: tl
+tags:
+- electra
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# ELECTRA Tagalog Small Uncased Generator
+Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
+
+This is the generator model used to sample synthetic text and pretrain the discriminator. Only use this model for retraining and mask-filling. For the actual model for downstream tasks, please refer to the discriminator models.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/electra-tagalog-small-uncased-generator', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-small-uncased-generator', do_lower_case=False)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/electra-tagalog-small-uncased-generator')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/electra-tagalog-small-uncased-generator', do_lower_case=False)
+```
+Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
+
+## Citations
+All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
+
+```
+@article{cruz2020investigating,
+  title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
+  author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
+  journal={arXiv preprint arXiv:2010.11574},
+  year={2020}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## Contact
+If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph